E11.5 (or E11?) mouse RPE
Run on 10x v3 platform
This is a trial run to determine whether Elan can process mouse tissue at this very early time point.
2022-05-10
Rscript -e 'library(rmarkdown); rmarkdown::render("00_process.Rmd", "html_document")'
Quick peek at Elan’s favorite Zfp503 and how which cluster it is differentially expressed in
library(tidyverse)
library(Seurat)
load('../data/00_process_output.Rdata')
seurat.markersCC <- seurat.markersCC %>% bind_rows(.id = 'cluster') %>% as_tibble(rownames = 'Gene') %>% mutate(Gene = gsub('\\.\\.\\.\\d+','',Gene))
# seurat.markersCC %>%
# group_by(cluster) %>% slice_max(avg_log2FC, n = 3, with_ties = FALSE) %>%
# DT::datatable()
seurat.markersCC %>% bind_rows() %>% filter(Gene == 'Zfp503')
For each cluster
To help roughly figure out which clusters as which cell types
library(clusterProfiler)
library(enrichplot)
eg_universe = bitr(seuratCC %>% row.names(), fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
'select()' returned 1:1 mapping between keys and columns
Warning in bitr(seuratCC %>% row.names(), fromType = "SYMBOL", toType = "ENTREZID", :
7.96% of input gene IDs are fail to map...
go_enrichCC <- list()
for (i in seurat.markersCC$cluster %>% unique()){
print(i)
diff_genes <- seurat.markersCC %>% filter(cluster == i)
eg_diff_genes <- bitr(diff_genes$Gene, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
eg_diff_genes <- diff_genes %>% left_join(., eg_diff_genes, by = c('Gene' = 'SYMBOL'))
go_enrichCC[[i]] <- enrichGO(gene = eg_diff_genes$ENTREZID,
universe = eg_universe$ENTREZID,
OrgDb = org.Mm.eg.db,
ont = "all",
pAdjustMethod = "BH",
pvalueCutoff = 0.01,
qvalueCutoff = 0.05,
readable = TRUE)
}
[1] "4"
'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID", :
1.12% of input gene IDs are fail to map...
[1] "2"
'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID", :
1.79% of input gene IDs are fail to map...
[1] "3"
'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID", :
2.88% of input gene IDs are fail to map...
[1] "0"
'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID", :
11.86% of input gene IDs are fail to map...
[1] "6"
'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID", :
1.96% of input gene IDs are fail to map...
[1] "1"
'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID", :
1.92% of input gene IDs are fail to map...
[1] "5"
'select()' returned 1:1 mapping between keys and columns
[1] "7"
'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID", :
0.48% of input gene IDs are fail to map...
[1] "8"
'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID", :
1.6% of input gene IDs are fail to map...
[1] "9"
'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID", :
4.84% of input gene IDs are fail to map...
[1] "12"
'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID", :
0.82% of input gene IDs are fail to map...
[1] "10"
'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID", :
1.39% of input gene IDs are fail to map...
[1] "13"
'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID", :
1.91% of input gene IDs are fail to map...
[1] "11"
'select()' returned 1:1 mapping between keys and columns
for (i in seurat.markersCC$cluster %>% unique() %>% sort()){
print(dotplot(go_enrichCC[[i]], showCategory=30) + ggtitle(glue::glue("Dotplot for cluster {i}")))
}
NA
NA
ct_cluster_CC <- rbind(c(0, "RPC"),
c(1, 'RPC'),
c(2, 'RPE Precursor'),
c(3, 'Fibroblast'),
c(4, 'Fibroblast'),
c(5, 'Optic Fissure'),
c(6, 'Proliferating RPC'),
c(7, 'Epithelial'),
c(8, 'Red Blood Cell'),
c(9, 'Corneal Progenitor'),
c(10, 'Blood Vessel'),
c(11, "PBMC"),
c(12, "NK/T"),
c(13, "Lens"
)) %>% data.frame()
colnames(ct_cluster_CC) <- c('seurat_clusters','CellType')
seuratCC@meta.data$CellType <- seuratCC@meta.data %>% dplyr::select(seurat_clusters) %>% left_join(ct_cluster_CC) %>% pull(CellType)
Joining, by = "seurat_clusters"
# ct_cluster <- c(0, "RPC",
# 1, 'Fibroblasts',
# 2, 'RPC',
# 3, 'RPE Precursor',
# 4, 'Cornea - JCT - TM?',
# 5, 'Optic Fissure',
# 6, 'Proliferating RPC',
# 7, 'Epithelial',
# 8, 'Red Blood Cell',
# 9, '?',
# 10, 'Vessel',
# 11, "Macrophage/Microglia")
With my poorly researched cell type assignments
DimPlot(seuratCC, reduction = "umap", group.by = 'CellType', label = TRUE, label.box = TRUE) +
scale_color_manual(values = pals::alphabet2() %>% unname()) +
scale_fill_manual(values = pals::alphabet2() %>% unname() )
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
DimPlot(seuratCC, group.by = 'Phase')
FeaturePlot(seuratCC, features = c("Pax2", "Ntn1", "Smoc2", "Vax1",
"Ttr", "Dct", "Rho", "Zfp503", "Mitf",
"Pax6"))
top_genes <- seurat.markersCC %>%
left_join(seuratCC@meta.data %>% as_tibble() %>%
dplyr::select(cluster = seurat_clusters, CellType) %>% unique()) %>%
group_by(cluster, CellType) %>% top_n(30, avg_log2FC) %>%
filter(cluster == 5) %>% head(15) %>% pull(Gene)
Joining, by = "cluster"
FeaturePlot(seuratCC, features = top_genes)
Zfp503 is overexpressed in both clusters 2 and 5
What other genes have this characteristic?
seurat.markersCC %>% filter(cluster %in% c(2,5), avg_log2FC > 0.7, p_val_adj < 0.01) %>%
group_by(Gene) %>%
summarise(Count = n(), avg_log2FC = mean(avg_log2FC)) %>%
filter(Count == 2)
FeaturePlot(seuratCC, features = seurat.markersCC %>% filter(cluster %in% c(2,5), avg_log2FC > 0.7, p_val_adj < 0.01) %>%
group_by(Gene) %>%
summarise(Count = n(), avg_log2FC = mean(avg_log2FC)) %>%
filter(Count == 2) %>%
pull(Gene))
Top 30 gene for each cluster (I was using this table along with the GO terms to guess each cell type for each cluster)
seurat.markersCC %>%
left_join(seuratCC@meta.data %>% as_tibble() %>%
dplyr::select(cluster = seurat_clusters, CellType) %>% unique()) %>%
group_by(cluster, CellType) %>% top_n(30, avg_log2FC) %>%
relocate(CellType) %>%
arrange(cluster) %>%
DT::datatable()
Joining, by = "cluster"
devtools::session_info()
─ Session info 🚰 🤴 🇨🇺 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
hash: potable water, prince, flag: Cuba
setting value
version R version 4.1.2 (2021-11-01)
os macOS Catalina 10.15.7
system x86_64, darwin17.0
ui RStudio
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2022-05-10
rstudio 2021.09.0+351 Ghost Orchid (desktop)
pandoc 2.14.0.3 @ /Applications/RStudio.app/Contents/MacOS/pandoc/ (via rmarkdown)
─ Packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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[1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
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